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Netlab Reference Manual mlpprior
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<H1> mlpprior
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<h2>
Purpose
</h2>
Create Gaussian prior for mlp.

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Synopsis
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<PRE>
prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2)</PRE>


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Description
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<CODE>prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2)</CODE> 
generates a data structure
<CODE>prior</CODE>, with fields <CODE>prior.alpha</CODE> and <CODE>prior.index</CODE>, which
specifies a Gaussian prior distribution for the network weights in a
two-layer feedforward network. Two different cases are possible. In
the first case, <CODE>aw1</CODE>, <CODE>ab1</CODE>, <CODE>aw2</CODE> and <CODE>ab2</CODE> are all
scalars and represent the regularization coefficients for four groups
of parameters in the network corresponding to first-layer weights,
first-layer biases, second-layer weights, and second-layer biases
respectively. Then <CODE>prior.alpha</CODE> represents a column vector of
length 4 containing the parameters, and <CODE>prior.index</CODE> is a matrix
specifying which weights belong in each group. Each column has one
element for each weight in the matrix, using the standard ordering as
defined in <CODE>mlppak</CODE>, and each element is 1 or 0 according to
whether the weight is a member of the corresponding group or not.  In
the second case the parameter <CODE>aw1</CODE> is a vector of length equal to
the number of inputs in the network, and the corresponding matrix
<CODE>prior.index</CODE> now partitions the first-layer weights into groups
corresponding to the weights fanning out of each input unit. This 
prior is appropriate for the technique of automatic relevance
determination. 

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See Also
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<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlperr.htm">mlperr</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE>, <CODE><a href="evidence.htm">evidence</a></CODE><hr>
<b>Pages:</b>
<a href="index.htm">Index</a>
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<p>Copyright (c) Ian T Nabney (1996-9)


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